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About Customer

Company: Leading utilities company, Based out of Toronto, Canada.
The client is a leading Smart Home Rental Program provider. They have more than 8500 happy customers enjoying energy solutions, smart homes, and automation. The company aims to reduce carbon emissions and bring environment-friendly solutions to their customers. Their programs are revolutionizing the everyday use of energy.

Customer Objectives

  • The client would receive thousands of calls every day inquiring about smart energy solutions.
  • They were facing a hard time generating qualified leads from this long list of callers - they have a prospect list of more than a million potential customers.
  • Manually calling the prospects and asking them pre-qualification questions was a tedious and time-consuming process for the sales representatives.

The client wanted to integrate their contact center solution with a Conversational Voicebot to achieve the following objectives:

Speech to Text Conversion

Text Analysis

Search Best response to provide incontext

Deliver Response witha text to speech conversion

Each of these steps requires running multiple AI models — so the time available for each individual network to execute if just ASR then 300ms or less, if entire service then 1 second, with 150+ concurrent requests.

Our Solutions

Our advanced NLP models enabled the platform to decipher company specific jargon leading to higher user satisfaction, ultimately leading to a qualified lead.

We leveraged our pre-built[/pre-trained] NLP and AI models [and fine tuned them on client data] to understand specific intent of prospects and delivers intelligent responses.

We leveraged our pre-built NLP and AI models to understand specific intent of prospects and delivers intelligent responses.

Also, our platform provides a Funnel View Analytics section, which records the stage at which the customer dropped the call so the company can continuously optimize the bot.

Impact

Our Deep-tech platform enabled:

  • we get responsesin around 162 ms, and our target was <= 300 ms.
  • average inference time is 40 ms for any ASR request.
  • 150+ concurrent ASR requests

 

The Results

Here is what Floatbot’s AI-powered Voicebot managed to do:

Helped the company generate a revenue of $400,000 through automated outbound calls within 2 months

Reduced customer support cost by40%

Generated 20% of total qualified leads

Customer Testimonial

We signed a contract with Floatbot to automate outbound calls and to generate qualified leads. We are extremely pleased with Floatbot platform capabilities and their Voice AI technology. In the last 2 months, we were able to generate revenue of $400,000+ through Floatbot's Voicebot.

At present, we are able to generate 20% of our total qualified leads through Voicebot. We aim to generate at least 80% of our total qualified leads through Floatbot's Voice AI platform, in the next 3 months

We got almost 24x7 support from Floatbot's team. I would highly recommend Floatbot to anyone looking for Voice AI to automate their contact center operations.

- Erica F,

Operations Manager,

Ontario based Utility Company, Canada